Binary Files Research Articles

Specifics of Data Collection and Data Processing during Formation of RailVista Dataset for Machine Learning- and Deep Learning-Based Applications.

This paper presents the methodology and outcomes of creating the Rail Vista dataset, designed for detecting defects on railway tracks using machine and deep learning techniques. The dataset comprises 200,000 high-resolution images categorized into 19 distinct classes covering various railway infrastructure defects. The data collection involved a meticulous process including complex image capture methods, distortion techniques for data enrichment, and secure storage in a data warehouse using efficient binary file formats. This structured dataset facilitates effective training of machine/deep learning models, enhancing automated defect detection systems in railway safety and maintenance applications. The study underscores the critical role of high-quality datasets in advancing machine learning applications within the railway domain, highlighting future prospects for improving safety and reliability through automated recognition technologies.

A Robust Parallel Computing Data Extraction Framework for Nanopore Experiments.

The success of a nanopore experiment relies not only on the quality of the experimental design but also on the performance of the analysis program utilized to decipher the ionic perturbations necessary for understanding the fundamental molecular intricacies. An event extraction framework is developed that leverages parallel computing, efficient memory management, and vectorization, yielding significant performance enhancement. The newly developed abf-ultra-simple function extracts key parameters from the header critical for the operation of open-seek-read-close data loading architecture running on multiple cores. This underpins the swift analysis of large files where an ≈×18 improvement is found for a 100min-long file (≈4.5GB) compared to the more traditional single (cell) array data loading method. The application is benchmarked against five other analysis platforms showcasing significant performance enhancement (>2×-1120×). The integrated provisions for batch analysis enable concurrently analyzing multiple files (vital for high-bandwidth experiments). Furthermore, the application is equipped with multi-level data fitting based on abrupt changes in the event waveform. The application condenses the extracted events to a single binary file improving data portability (e.g., 16GB file with 28182 events reduces to 47.9MB-343× size reduction) and enables a multitude of post-analysis extractions to be done efficiently.

Code Clones: A Novel Approach to Detecting Plagiarism in Binary Decomposition of C Programs

Abstract The escalation of code plagiarism in computer science education has necessitated the development of more sophisticated detection methods, particularly for binary files which pose a unique challenge. This study introduces a novel approach to plagiarism detection through binary decomposition, which decompiles binary files back into high-level code to reveal similarities that may be hidden at the binary level. The methodology is crucial for scenarios where students submit compiled programs, enabling the application of traditional code similarity analysis methods to detect instances of plagiarism. Statistical data on code similarity among student submissions in a C programming course are presented, highlighting the urgent need for robust detection strategies. This paper explores the ethical implications of plagiarism, the technical challenges of binary file analysis, and the potential for applying this technique across various programming languages and educational contexts.

RepFTI: Representation-Fused Function-Type Inference for Vehicular Secure Software Systems

To enhance the security of vehicular software systems, inversely identifying the underlying function types of binary files plays a key role in threat discovery. However, existing function-type inference (FTI) methods can only provide a suboptimal performance because of splitting binary files into multiple sub-blocks as inputs, which results in breaking the program context logic and complete data dependency. To solve this problem, we propose a novel representation-fused function-type inference (RepFTI) framework for secure vehicular software systems. First, the RepFTI learns semantic representations of assembly codes and then extracts node representations in the function call graph by the multi-head attention mechanism of Graph-Attention Transformer (GAT) models. Second, the RepFTI fuses these representations to accurately infer the function type. With RepFTI, the specific limits of in-vehicle software will be bypassed, which proposes a promising direction for other work that relies on reverse engineering to improve software security.

Scalepopgen: Bioinformatic Workflow Resources Implemented in Nextflow for Comprehensive Population Genomic Analyses.

Population genomic analyses such as inference of population structure and identifying signatures of selection usually involve the application of a plethora of tools. The installation of tools and their dependencies, data transformation, or series of data preprocessing in a particular order sometimes makes the analyses challenging. While the usage of container-based technologies has significantly resolved the problems associated with the installation of tools and their dependencies, population genomic analyses requiring multistep pipelines or complex data transformation can greatly be facilitated by the application of workflow management systems such as Nextflow and Snakemake. Here, we present scalepopgen, a collection of fully automated workflows that can carry out widely used population genomic analyses on the biallelic single nucleotide polymorphism data stored in either variant calling format files or the plink-generated binary files. scalepopgen is developed in Nextflow and can be run locally or on high-performance computing systems using either Conda, Singularity, or Docker. The automated workflow includes procedures such as (i) filtering of individuals and genotypes; (ii) principal component analysis, admixture with identifying optimal K-values; (iii) running TreeMix analysis with or without bootstrapping and migration edges, followed by identification of an optimal number of migration edges; (iv) implementing single-population and pair-wise population comparison-based procedures to identify genomic signatures of selection. The pipeline uses various open-source tools; additionally, several Python and R scripts are also provided to collect and visualize the results. The tool is freely available at https://github.com/Popgen48/scalepopgen.

The Origins of the Generic Mapping Tools: From Table Tennis to Geoscience

AbstractThe Generic Mapping Tools (GMT) is one of the most used toolsets in the Earth, Ocean, and Planetary sciences, originating as far back as the 1980s. It is an early example of an open‐source software code modeled after contemporaneous UNIX tools, and it was one of the first to employ PostScript as its graphics language and netCDF for binary files to ensure portability across different computing platforms. Here I trace the origin and evolution of GMT to the present day. The additions of MATLAB, Python, and Julia wrappers around the GMT C Application Program Interface (API) are now introducing GMT to numerous new and younger users and the platform shows no sign of diminishing after almost 40 years; in fact, usage continues to expand. Pursuing GMT for fun (and funding) has positively affected other areas of my scientific interests, and my new research modules continue to be added to GMT. The future holds many promises but will require formation and leadership of communities to steer and maintain the essential science tools that have served us well for many decades.

A Vulnerability Scanning Method for Web Services in Embedded Firmware

As the Internet of Things (IoT) era arrives, the proliferation of IoT devices exposed to the Internet presents a significant challenge to device security. Firmware is software that operates within Internet of Things (IoT) devices, directly governing their behaviors and functionalities. Consequently, the security of firmware is critical to shielding IoT devices from potential threats. In order to enable users to operate a device intuitively, firmware commonly provides a web interface. Consequently, this interface frequently serves as the primary attack goal in Internet of Things (IoT) devices, rendering them susceptible to numerous cyber-attacks. Unfortunately, web services have complex data interactions and implicit dependencies, and it is not easy to balance efficiency and accuracy during the analysis process, leading to heavy overhead. This paper proposes a lightweight vulnerability scanning approach, WFinder, designed explicitly for embedded firmware web services to perform vulnerability checks on backend binary files in firmware. WFinder uses static analysis to focus on identifying vulnerabilities in boundary binary files related to web services in firmware. Initially, the approach identifies boundary binary files and external data entry points based on front-end and back-end associativity features. Subsequently, rules are formulated to filter hazardous functions to narrow the analysis targets. Finally, the method generates sensitive call paths from the external data input points to the hazardous functions and conducts a lightweight taint analysis along these paths to uncover potential vulnerabilities. We implemented a prototype of WFinder and evaluated it on the firmware of ten devices from five well-known manufacturers. We discovered thirteen potential vulnerabilities, eight of which were confirmed by the CNVD, and assigned them CNVD identification numbers. Compared with the most advanced tool, SATC, WFinder was more efficient at discovering more bugs on the test set. These results indicate that WFinder is effective at detecting bugs in embedded web services.

Forensic analysis of iOS binary cookie files.

iPhone operating system (iOS) devices utilize binary cookies as a data storage tool, encoding user-specific information within an often-neglected element of smartphone analysis. This binary format contains details such as cookie flags, expiration, and creation dates, domain, and value of the cookie. These data are invaluable for forensic investigations. This study presents a comprehensive methodology to decode and extract valuable data from these files, enhancing the ability to recover user activity information from iOS devices. This paper provides an in-depth forensic investigation into the structure and function of iOS binary cookie files. Our proposed forensic technique includes a combination of reverse engineering and custom-built Python scripts to decode the binary structure. The results of our research demonstrate that these cookie files can reveal an array of important digital traces, including user preferences, visited websites, and timestamps of online activities. It concludes that the forensic analysis of iOS binary cookie files can be a tool for forensic investigators and cybersecurity professionals. In the rapidly evolving domain of digital forensics, this research contributes to our understanding of less-explored data sources within iOS devices and their potential value in investigative contexts.

Fasta2Structure: a user-friendly tool for converting multiple aligned FASTA files to STRUCTURE format

BackgroundThe STRUCTURE software has gained popularity as a tool for population structure and genetic analysis. Nevertheless, formatting data to meet STRUCTURE's specific requirements can be daunting and susceptible to errors, especially when handling multilocus data. This article highlights the creation of a graphical user interface (GUI) application tailored to streamline the process of converting multiple sequence alignments into a single, cohesive file that is compatible with the STRUCTURE software.ResultsThe application has been developed utilizing Tkinter for the GUI and Biopython for handling FASTA files. This program processes the files, pinpoints variable sites, and converts the sequences into a binary format. Subsequently, the sequences are concatenated and presented within the graphical interface's text area, enabling users to review and confirm the results. Furthermore, the program stores the concatenated results in a file, delivering a ready-to-use input for the STRUCTURE software.ConclusionThis application offers an efficient and dependable solution for transforming multiple aligned FASTA files into a concatenated binary format file, which is compatible with the STRUCTURE software. With its user-friendly graphical interface and error-reduction approach, this tool proves invaluable for researchers engaged in population structure and genetic analysis.

Photoelectricity Theory-Based Concrete Crack Image Segmentation and Optimal Exposure Interval Research

To solve the problem of low accuracy in automatic concrete crack image segmentation and the non-standardization of concrete crack image datasets, an exposure-based concrete crack image capture scene characterization method was proposed, and the optimal exposure interval for crack segmentation was presented by multiple scene image capture experiments. First, current public crack datasets were collected and analyzed, and it was shown that improper spatial resolution, mislabeling, overexposure, and defocus are frequent non-standardization problems in crack dataset production. Through the analysis of the photoelectric principle in concrete crack imaging, an equivalent exposure was set as a core indicator for scene characterization. Twenty-one indoor scenes were designed by varying the illumination intensity and exposure time, and the experimental results showed that an equivalent exposure can be a core control index for scene characterization. The grayscale distribution law of concrete crack images was analyzed with four specimens’ images captured indoors in 50 exposure scenes, and the segmentation accuracy of an image from each scene was calculated through comparison with corresponding manually labeled binary files. The experiment’s results revealed that 5~50 lx·s was the optimal equivalent exposure interval for concrete crack image segmentation, in which better segmentation accuracy was achieved with an F1 score of up to 96.3%.

Memory Usage Estimation for Dataflow-Model-Based Software Development Methodology

In designing software on an embedded hardware platform with limited memory size, it is critical to guarantee that the peak memory requirement does not exceed the memory capacity. We propose a memory usage estimation technique in a model-based software development methodology where the application code is synthesized from a dataflow model-based specification. To minimize the degree of overestimation without running the synthesized software, the proposed technique analyzes the binary file and shared library dependencies of dataflow tasks. We verify the proposed technique in developing real-life applications on Linux and bare-metal systems.

GraphMoCo: A graph momentum contrast model for large-scale binary function representation learning

In the field of cybersecurity, the ability to compute similarity scores at the function level for binary code is of utmost importance. Considering that a single binary file may contain an extensive amount of functions, an effective learning framework must exhibit both high accuracy and efficiency when handling substantial volumes of data. Nonetheless, conventional methods encounter several limitations. Firstly, accurately annotating different pairs of functions with appropriate labels poses a significant challenge, thereby making it difficult to employ supervised learning methods without risk of overtraining. Secondly, while SOTA models often rely on pre-trained encoders or fine-grained graph comparison techniques, these approaches suffer from drawbacks related to time and memory consumption. Thirdly, the momentum update algorithm utilized in graph-based contrastive learning models can result in information leakage. Surprisingly, none of the existing articles address this issue. This research focuses on addressing the challenges associated with large-scale Binary Code Similarity Detection (BCSD). To overcome the aforementioned problems, we propose GraphMoCo: a graph momentum contrast model that leverages multimodal structural information for efficient binary function representation learning on a large scale. We adopt an unsupervised learning strategy. Our approach eliminates the need for manual labeling. By leveraging the intrinsic structural information at multiple levels of the binary code, our model could achieve higher accuracy with a simple CNN-based model. By introducing the preshuffle mechanism, the issue of information leakage in graph momentum update algorithm is mitigated. The evaluation results indicate that GraphMoCo exhibits superior performance compared to SOTA approaches in the function pair search task, showing an average improvement of 7% on AUC, and 10% on MRR and Recall@1. Furthermore, GraphMoCo achieves a MAP of 0.93 on the more challenging dataset 2, which comprises a larger function pool. In a real-world scenario, specifically in known vulnerability searching, GraphMoCo achieves a MRR that surpasses existing SOTA models by 5%.

Aweak phenotype associated with novel ABO*A allele variant c.106delinsGG.

Discrepancy between forward and reverse ABO grouping could be due to several reasons including genetic mutations of the alleles encoding group specific transferase. The healthy donors found with weak A antigen were investigated to ascertain the allele responsible for variation. Standard serological methods were employed using commercial antisera. The molecular sequencing was performed on DNA with enrichment library prep kit and a custom designed overlapping probe panel. Binary alignment mapping files, generated on board the Illumina MiSeq instrument and aligned to the GRCh37/Hg19 reference genome, were uploaded to the QIAGEN CLC genomics workbench software (version. 20) where variant call files were generated and analyzed. Red blood cells (RBCs) of six healthy donors, showing weak mix-field agglutination by anti-A and anti-A, B and plasma with absence or weakly reacting anti-A, were investigated serologically. The RBCs incubated with anti-A yield positive elution and their saliva lacked A but possessed H antigen thereby classifying as a historical known phenotype Aend. Family study on 4 probands showed inheritance of the trait. Molecular studies revealed presence of ABO*A allele carrying rare novel variant referred to as c.106delinsGG in line with HGVS recommendation that was thought to be responsible for the variant of A. Six cases serologically defined as Aweak were found to be associated with novel allele ABO*A (c.106delinsGG). The Aweak phenotype with the novel allele has not been displayed on International Society of Blood Transfusion database, though c.106delinsGG is listed in the UCSC genome browser under rs782544248.

Analysis of ChatGPT's capabilities for solving problems of reverse-engineering of software

The article presents the results of the analysis and evaluation of the capabilities of the open version of the chatbot with artificial intelligence ChatGPT 3.5 for solving typical problems of software reverse engineering. Three classes of reverse engineering tasks were selected for analysis: source code analysis, binary code analysis, and data models analysis. In each class of tasks, the most typical tasks were selected taking into account the limitations of ChatGPT regarding the processing of graphical models and the amount of input and output data, and sets of test tasks were developed for each task. As an approach to assessment, an approach similar to the assessment of competencies of higher education applicants after studying the relevant discipline was chosen. The following were considered as criteria for evaluating answers to test tasks: correctness (rightness, coincidence with expectations); completeness (obtaining the final result); accuracy (solving the task without additional questions); reasonableness (availability of explanations and answers to questions). The following scale was used and described for evaluations: excellent, very good, good, satisfactory, sufficient, unsatisfactory. During the testing for each test task, tasks statement and all necessary data were entered through the ChatGPT interface. As a result of the analysis, it was determined that ChatGPT better solves the problems of source code analysis (excellent and very good grades were obtained for semantic and structural analysis, restoration of mathematical support, quality assessment, security audit and refactoring, as well as for conversion to another programming language), tasks of decompilation of IDA pseudocode into complete C source code, tasks of reverse engineering of relational databases, and tasks of generating YARA rules for recognizing file formats. Unsatisfactory grades were obtained for dynamic analysis of assembly code and determination of binary file structures of non-standard formats. ChatGPT solves the rest of the problems well and satisfactorily, but requires checking the results, clarifying queries and prompts, as well as manual error correction in some cases. ChatGPT errors were observed when analyzing binary data represented by hexadecimal symbols, as well as errors in generated scripts for programming in IDA. On the basis of the set grades, conclusions were made regarding the expediency, possibility, or impracticality of using ChatGPT to solve each type of software reverse engineering problems, and appropriate recommendations were also provided. Prospects for further research include testing new versions of ChatGPT and other similar artificial intelligence systems regarding the capabilities of analyzing and synthesizing graphical models of software.

AAHEG: Automatic Advanced Heap Exploit Generation Based on Abstract Syntax Tree

Automatic Exploit Generation (AEG) involves automatically discovering paths in a program that trigger vulnerabilities, thereby generating exploits. While there is considerable research on heap-related vulnerability detection, such as detecting Heap Overflow and Use After Free (UAF) vulnerabilities, among contemporary heap-automated exploit techniques, only certain automated exploit techniques can hijack program control flow to the shellcode. An important limitation of this approach is that it cannot effectively bypass Linux’s protection mechanisms. To solve this problem, we introduced Automatic Advanced Heap Exploit Generation (AAHEG). It first applies symbolic execution to analyze heap-related primitives in files and then detects potential heap-related vulnerabilities without a source code. After identifying these vulnerabilities, AAHEG builds an exploit abstract syntax tree (AST) to identify one or more successful exploit strategies, such as fast bin attack and Safe-unlink. AAHEG then selects exploitable methods via an abstract syntax tree (AST) and performs final testing to produce the final exploit. AAHEG chose to generate advanced heap-related exploits because the exploits can bypass Linux protections. Basically, AAHEG can automatically detect heap-related vulnerabilities in binaries without source code, build an exploit AST, choose from a variety of advanced heap exploit methods, bypass all Linux protection mechanisms, and generate final file-form exploit based on pwntools which can pass local and remote testing. Experimental results show that AAHEG successfully completed vulnerability detection and exploit generation for 20 Capture The Flag (CTF) binary files, 11 of which have all protection mechanisms enabled.

Chewing Analysis by Means of Electromagnetic Articulography: Current Developments and New Possibilities

Chewing is a complex procedure that involves sensory feedback and motor impulses controlled by the trigeminal system in the brainstem. The analysis of mandibular movement is a first approximation to understanding these mechanisms. Several recording methods have been tested to achieve this. Video, ultrasound, the use of external markers and kinesiographs are examples of recording systems used in research. Electromagnetic articulography is an alternative method to those previously mentioned. It consists of the use of electromagnetic fields and receiver coils. The receiver coils are placed on the points of interest and the 3D coordinates of movement are saved in binary files. In the Oral Physiology Laboratory of the Dental Sciences Research Center (Centro de Investigación en Ciencias Odontológicas—CICO), in the Faculty of Dentistry at the Universidad de La Frontera (Temuco, Chile) several research studies have been carried out using the AG501 3D EMA articulograph (Carstens Medizinelektronik, Lenglern, Germany). With this device, they developed a series of protocols to record mandibular movement and obtain new information, such as the 3D Posselt polygon, the area of each polygon, individualized masticatory cycles and speed and acceleration profiles. Other investigations have analyzed these parameters, but separately. The AG501 allows for holistic analysis of all these data without altering natural movement. A limitation of this technology is the interference generated by its metallic elements. The aim of the present work is to show the developed methods used to record mandibular movement in the CICO, using the AG501 and compare them with others used in several research studies.

Web Framework

The purpose of this paper is to demonstrate how to extend a web framework (WF) for developing web apps that can meet the diverse needs of different fields. This work aims to provide computer science senior project students with an opportunity to work on a wide range of projects using Apache, HTML, CSS, JavaScript, PHP, and MySQL. The paper presents a WF that manages jokes, based on the book PHP MySQL: Novice to Ninja (6th ed.) by T. Butler and K. Yank. The WF is interactive, responsive, secure, password-protected and database-driven. It consists of generic and specific components that use a MySQL database on the web server-side to store jokes. The specific components can be modified or added to create custom apps. Admin users can log in with their email and password and perform various actions (view, add, delete and edit) on jokes and categories, depending on their level of permission. Admins with full permissions can also edit the permissions of other registered accounts and access the Manage Categories, Manage Jokes, and Manage Authors pages. The original WF is enhanced and expanded for the senior project starting point by adding the following features: a hierarchical main menu with admin menus as the second level, a highlighted selected menu item, responsive design - accessibility on all devices, improved password security (minimum of 8 characters, 1 lowercase letter, 1 uppercase letter, 1 digit, 1 special character), a two-factor authentication security subsystem, cascaded deletion and uploaded and retrieved binary files (images and documents). In conclusion, the project is designed and implemented following the best practices and tested on a web hosting server provider.

Similarity Regression Of Functions In Different Compiled Forms With Neural Attentions On Dual Control-Flow Graphs

Abstract Detecting if two functions in different compiled forms are similar has a wide range of applications in software security. We present a method that leverages both semantic and structural features of functions, learned by a neural-net model on the underlying control-flow graphs (CFGs). In particular, we devise a neural function-similarity regressor (NFSR) with attentions on dual CFGs. We train and evaluate NFSR on a dataset consisting of nearly 4 million functions from over 14 900 binary files. Experiments show that NFSR is superior to the SOTA models of SAFE, Gemini and GMN, especially for binary functions with large CFGs. An ablation study shows that attention on dual CFGs plays a significant role in detecting function similarities.

Optimal Planning Target Margin for Prostate Radiotherapy Based on Interfractional and Intrafractional Variability Assessment during 1.5T MR-Guided Radiotherapy.

MR-guided radiotherapy (MRgRT) provides superior soft-tissue contrast over CT-based image guidance. We collected and analyzed daily pre-treatment (PRE) and real-time motion-monitoring (MM) MR images of patients receiving prostate radiotherapy to assess interfractional and intrafractional variability of prostate using two localization methods: pelvic bony anatomy (bone) and prostate during online adaptive radiotherapy (ART). PRE and MM MRIs for the first five fractions of twenty prostate cancer patients who received definitive MRgRT with 1.5T MRI were collected. Using MIM software, rigid registration between PRE MRI and planning CT images based on pelvic bony anatomy and prostate reproduced bone localization and online ART, respectively. To determine interfractional setup margin (SM), prostate was delineated on all PRE MRIs registered after bone and prostate localizations by a radiation oncologist, and centroid values of prostate contours between planning CT and PRE MRIs were compared. To determine interobserver variability, another radiation oncologist, a medical physicist, and a radiotherapist contoured prostate for both localization methods. For internal margin (IM) assessment, we used MM MRIs of the five patients who had all three sets of coronal, sagittal, and axial cine images and determined the maximum contour displacement using in-house MATLAB-based software converting binary image files to 2D cine images with a superimposed grid of 1 mm spacing. A total of 100 PRE and 25 MM MRIs were analyzed. Four hundred prostate contours were delineated on MR images registered with planning CT based on both bony anatomy and prostate. After bone localization, SM was 0.57±0.42 mm in left-right (LR), 2.45±1.98 mm in anterior-posterior (AP), and 2.28±2.08 mm in superior-inferior (SI) directions, and IO was 1.06±0.58 mm in LR, 2.32±1.08 mm in AP, and 3.30±1.85 mm in SI directions. After prostate localization, SM was 0.76±0.57 mm in LR, 1.89±1.60 mm in AP, and 2.2±1.79 mm in SI directions, and IO was 1.11±0.55 mm in LR, 2.13±1.07 mm in AP, and 3.53±1.65 mm in SI directions. Average IM was 2.12±0.86 mm, 2.24±1.07 mm, and 2.84±0.88 mm in LR, AP, and SI directions, respectively. Using daily MRIs from MRgRT, we showed that movements in the SI direction were the largest source of variability in prostate definitive RT. In addition, interobserver variability was a non-negligible source of margin. Optimal PTV margin should also consider internal margin, especially in the SI direction.

TERSE/PROLIX (TRPX) - a new algorithm for fast and lossless compression and decompression of diffraction and cryo-EM data.

High-throughput data collection in crystallography poses significant challenges in handling massive amounts of data. Here, TERSE/PROLIX (or TRPX for short) is presented, a novel lossless compression algorithm specifically designed for diffraction data. The algorithm is compared with established lossless compression algorithms implemented in gzip, bzip2, CBF (crystallographic binary file), Zstandard(zstd), LZ4 and HDF5 with gzip, LZF and bitshuffle+LZ4 filters, in terms of compression efficiency and speed, using continuous-rotation electron diffraction data of an inorganic compound and raw cryo-EM data. The results show that TRPX significantly outperforms all these algorithms in terms of speed and compression rate. It was 60 times faster than bzip2 (which achieved a similar compression rate), and more than 3 times faster than LZ4, which was the runner-up in terms of speed, but had a much worse compression rate. TRPX files are byte-order independent and upon compilation the algorithm occupies very little memory. It can therefore be readily implemented in hardware. By providing a tailored solution for diffraction and raw cryo-EM data, TRPX facilitates more efficient data analysis and interpretation while mitigating storage and transmission concerns. The C++20 compression/decompression code, custom TIFF library and an ImageJ/Fiji Java plugin for reading TRPX files are open-sourced on GitHub under the permissive MIT license.